SciPy Recipes :: a cookbook with over 110 proven recipes for performing mathematical and scientific computations.
With the SciPy Stack, you get the power to effectively manipulate and process your data using the popular Python language. This book will show you how to get the most out of the SciPy Stack to get a better sense of your data. It includes hands-on recipes for using the different components of the Sci...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2017.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | With the SciPy Stack, you get the power to effectively manipulate and process your data using the popular Python language. This book will show you how to get the most out of the SciPy Stack to get a better sense of your data. It includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib ... |
| Beschreibung: | 1 online resource (381 pages) |
| ISBN: | 9781788295819 1788295811 1788291468 9781788291460 |
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| 100 | 1 | |a Ayyadevara, Kishore. | |
| 245 | 1 | 0 | |a SciPy Recipes : |b a cookbook with over 110 proven recipes for performing mathematical and scientific computations. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2017. | ||
| 300 | |a 1 online resource (381 pages) | ||
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| 520 | |a With the SciPy Stack, you get the power to effectively manipulate and process your data using the popular Python language. This book will show you how to get the most out of the SciPy Stack to get a better sense of your data. It includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib ... | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting to Know the Tools -- Introduction -- Installing Anaconda on Windows -- How to do it ... -- Installing Anaconda on macOS -- How to do it ... -- Installing Anaconda on Linux -- How to do it ... -- Checking the Anaconda installation -- How to do it ... -- Installing SciPy from a binary distribution on Windows -- How to do it ... -- Installing Python -- Installing the SciPy stack -- Installing SciPy from a binary distribution on macOS -- How to do it ... -- Installing the Xcode command-line tools -- Installing Homebrew -- Installing Python 3 -- Installing the SciPy stack -- Installing SciPy from source on Linux -- How to do it ... -- Installing Python 3 -- Installing the SciPy stack -- Installing optional packages with conda -- Getting ready -- How to do it ... -- Installing packages with pip -- How to do it ... -- Setting up a virtual environment with conda -- Getting ready -- How to do it ... -- Creating a virtual environment for development with conda -- Getting ready -- How to do it ... -- Creating a conda environment with a different version of a package -- Getting ready -- How to do it ... -- Using conda environments to run different versions of Python -- Getting ready -- How to do it ... -- Creating virtual environments with venv -- How to do it ... -- Running SciPy in a script -- Getting ready -- How to do it ... -- Running SciPy in Jupyter -- Getting ready -- How to do it ... -- Running SciPy in Spyder -- Getting ready -- How to do it ... -- Running SciPy in PyCharm -- Getting started -- How to do it ... -- Chapter 2: Getting Started with NumPy -- Introduction -- Creating NumPy arrays -- How to do it ... -- Creating an array from a list. | |
| 505 | 8 | |a Specifying the data type for elements in an array -- Creating an empty array with a given shape -- Creating arrays of zeros and ones with a single value -- Creating arrays with equally spaced values -- Creating an array by repeating elements -- Creating an array by tiling another array -- Creating an array with the same shape as another array -- Using object arrays to store heterogeneous data -- See also -- Querying and changing the shape of an array -- How to do it ... -- Storing and retrieving NumPy arrays -- How to do it ... -- Storing a NumPy array in text format -- Storing a NumPy array in CSV format -- Loading an array from a text file -- Storing a single array in binary format -- Storing several arrays in binary format -- Loading arrays stored in NPY binary format -- Indexing -- How to do it ... -- Accessing sub arrays using slices -- Selecting subarrays using an index list -- Indexing with Boolean arrays -- Operations on arrays -- How to do it ... -- Computing a function for all elements of an array -- Doing array operations -- Computing matrix products -- Using masked arrays to represent invalid data -- How to do it ... -- Creating a masked array from an explicit mask -- Creating a masked array from a condition -- Using object arrays to store heterogeneous data -- How to do it ... -- Defining, symbolically, a function operating on arrays -- Getting ready -- How to do it ... -- How it works ... -- Chapter 3: Using Matplotlib to Create Graphs -- Introduction -- Creating two-dimensional plots of functions and data -- Getting ready -- How to do it ... -- How it works ... -- Generating multiple plots in a single figure -- Getting ready -- How to do it ... -- How it works ... -- Setting line styles and markers -- Getting ready -- How to do it ... -- How it works ... -- Using different backends to display graphs -- Getting ready -- How to do it ... -- How it works ... | |
| 505 | 8 | |a Saving plots to disk -- Getting ready -- How to do it ... -- How it works ... -- Annotating graphs -- Getting ready -- How to do it ... -- How it works ... -- Generating histograms and box plots -- Getting ready -- How to do it ... -- How it works ... -- Creating three-dimensional plots -- Getting ready -- How to do it ... -- How it works ... -- Generating interactive displays in the Jupyter Notebook -- Getting ready -- How to do it ... -- How it works ... -- Object-oriented graph creation using Artist objects -- Getting ready -- How to do it ... -- How it works ... -- Creating a map with cartopy -- Getting ready -- How to do it ... -- How it works ... -- Chapter 4: Data Wrangling with pandas -- Creating Series objects -- Getting ready -- How to do it ... -- How it works ... -- Creating DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Inserting and deleting columns to a DataFrame -- Getting ready -- How to do it ... -- How it works ... -- Inserting and deleting rows to a DataFrame -- Getting ready -- How to do it ... -- How it works ... -- Selecting items by row indexes and column labels -- Getting ready -- How to do it ... -- How it works ... -- Selecting items by integer location -- Getting ready -- How to do it ... -- How it works ... -- Selecting items using mixed indexing -- Getting ready -- How to do it ... -- How it works ... -- Accessing, selecting, and modifying data -- Getting ready -- How to do it ... -- How it works ... -- Selecting rows using Boolean selection -- How to do it ... -- Reading and storing data in different formats -- Getting ready -- How to do it ... -- Working with CSV, text/tabular, and format data -- How it works ... -- Reading a CSV file into a DataFrame -- Specifying the index column when reading a CSV file -- Reading and writing data in Excel format -- Reading and writing JSON files -- Reading HTML data from the web -- Accessing CSV data on the web. | |
| 505 | 8 | |a Reading and writing from/to SQL databases -- Data displays employing different kinds of visual representation -- Getting ready -- How to do it ... -- How it works ... -- How to apply numerical functions and operations to Series and DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Computing statistical functions on Series and DataFrame objects -- Getting ready -- How to do it ... -- Retrieving summary descriptive statistics -- How it works ... -- Calculating the mean -- Calculating variance and standard deviation -- How to sort data in Series and DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Performing merging, joins, concatenation, and grouping -- Getting ready -- How to do it ... -- How it works ... -- Merging data from multiple pandas objects -- Chapter 5: Matrices and Linear Algebra -- Introduction -- Matrix operations and functions on two-dimensional arrays -- How to do it ... -- Solving linear systems using matrices -- How it works ... -- How to do it ... -- Calculating the null space of a matrix -- How to do it ... -- Calculating the LU decompositions of a matrix -- How to do it ... -- Calculating the QR decomposition of a matrix -- How to do it ... -- Calculating the eigenvalue and eigenvector of a matrix -- How to do it ... -- Diagonalizing a matrix -- How to do it ... -- Calculating the Jordan form of a matrix -- How to do it ... -- Calculating the singular value decomposition of a matrix -- How to do it ... -- Creating a sparse matrix -- How to do it ... -- Computations on top of a sparse matrix -- How to do it ... -- Chapter 6: Solving Equations and Optimization -- Introduction -- Non-linear equations and systems -- Getting ready -- How to do it ... -- How it works ... -- System of equations and how to solve it -- Getting ready -- How to do it ... -- How it works ... -- Choosing the solver used to find the solution of equations. | |
| 505 | 8 | |a Getting ready -- How to do it ... -- How it works ... -- Solving constrained non-linear optimization problems in several variables -- Getting ready -- How to do it ... -- How it works ... -- Solving one-dimensional optimization problems -- Getting ready -- How to do it ... -- How it works ... -- Solving multidimensional non-linear equations using the Newton-Krylov method -- Getting ready -- How to do it ... -- Solving multidimensional non-linear equations using the Anderson method -- Getting ready -- How to do it ... -- How it works ... -- Finding the best linear fit for a set of data -- Getting ready -- How to do it ... -- How it works ... -- Doing non-linear regression for a set of data -- Getting ready -- How to do it ... -- How it works ... -- Regression -- Getting ready -- How to do it ... -- How it works ... -- Chapter 7: Constants and Special Functions -- Introduction -- Physical and mathematical constants available in SciPy -- Getting ready ... -- How to do it ... -- Using constants in the CODATA database -- Getting ready -- How to do it ... -- Bessel functions -- Getting ready ... -- How to do it ... -- Error functions -- Getting ready ... -- How to do it ... -- Orthogonal polynomials functions -- Getting ready ... -- How to do it ... -- Gamma function -- Getting ready ... -- How to do it ... -- How it works ... -- The Riemann zeta function -- Getting ready -- How to do it ... -- How it works ... -- Airy and Bairy functions -- Getting ready ... -- How to do it ... -- The Bessel and Struve functions -- Getting ready ... -- How to do it ... -- How it works ... -- There's more -- Chapter 8: Calculus, Interpolation, and Differential Equations -- Introduction -- Integration -- Getting ready -- How to do it ... -- How it works ... -- Computing integrals using the Newton-Cotes method -- Computing integrals using a Gaussian quadrature -- Getting ready -- How to do it ... | |
| 650 | 0 | |a Python. | |
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| 650 | 7 | |a Numerical analysis |2 fast | |
| 700 | 1 | |a Martins, Luiz Felipe. | |
| 700 | 1 | |a Oliva Ramos, Ruben. | |
| 700 | 1 | |a Oliva, Tomas. | |
| 700 | 1 | |a Wu, Ke. | |
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| contents | Cover -- Title Page -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting to Know the Tools -- Introduction -- Installing Anaconda on Windows -- How to do it ... -- Installing Anaconda on macOS -- How to do it ... -- Installing Anaconda on Linux -- How to do it ... -- Checking the Anaconda installation -- How to do it ... -- Installing SciPy from a binary distribution on Windows -- How to do it ... -- Installing Python -- Installing the SciPy stack -- Installing SciPy from a binary distribution on macOS -- How to do it ... -- Installing the Xcode command-line tools -- Installing Homebrew -- Installing Python 3 -- Installing the SciPy stack -- Installing SciPy from source on Linux -- How to do it ... -- Installing Python 3 -- Installing the SciPy stack -- Installing optional packages with conda -- Getting ready -- How to do it ... -- Installing packages with pip -- How to do it ... -- Setting up a virtual environment with conda -- Getting ready -- How to do it ... -- Creating a virtual environment for development with conda -- Getting ready -- How to do it ... -- Creating a conda environment with a different version of a package -- Getting ready -- How to do it ... -- Using conda environments to run different versions of Python -- Getting ready -- How to do it ... -- Creating virtual environments with venv -- How to do it ... -- Running SciPy in a script -- Getting ready -- How to do it ... -- Running SciPy in Jupyter -- Getting ready -- How to do it ... -- Running SciPy in Spyder -- Getting ready -- How to do it ... -- Running SciPy in PyCharm -- Getting started -- How to do it ... -- Chapter 2: Getting Started with NumPy -- Introduction -- Creating NumPy arrays -- How to do it ... -- Creating an array from a list. Specifying the data type for elements in an array -- Creating an empty array with a given shape -- Creating arrays of zeros and ones with a single value -- Creating arrays with equally spaced values -- Creating an array by repeating elements -- Creating an array by tiling another array -- Creating an array with the same shape as another array -- Using object arrays to store heterogeneous data -- See also -- Querying and changing the shape of an array -- How to do it ... -- Storing and retrieving NumPy arrays -- How to do it ... -- Storing a NumPy array in text format -- Storing a NumPy array in CSV format -- Loading an array from a text file -- Storing a single array in binary format -- Storing several arrays in binary format -- Loading arrays stored in NPY binary format -- Indexing -- How to do it ... -- Accessing sub arrays using slices -- Selecting subarrays using an index list -- Indexing with Boolean arrays -- Operations on arrays -- How to do it ... -- Computing a function for all elements of an array -- Doing array operations -- Computing matrix products -- Using masked arrays to represent invalid data -- How to do it ... -- Creating a masked array from an explicit mask -- Creating a masked array from a condition -- Using object arrays to store heterogeneous data -- How to do it ... -- Defining, symbolically, a function operating on arrays -- Getting ready -- How to do it ... -- How it works ... -- Chapter 3: Using Matplotlib to Create Graphs -- Introduction -- Creating two-dimensional plots of functions and data -- Getting ready -- How to do it ... -- How it works ... -- Generating multiple plots in a single figure -- Getting ready -- How to do it ... -- How it works ... -- Setting line styles and markers -- Getting ready -- How to do it ... -- How it works ... -- Using different backends to display graphs -- Getting ready -- How to do it ... -- How it works ... Saving plots to disk -- Getting ready -- How to do it ... -- How it works ... -- Annotating graphs -- Getting ready -- How to do it ... -- How it works ... -- Generating histograms and box plots -- Getting ready -- How to do it ... -- How it works ... -- Creating three-dimensional plots -- Getting ready -- How to do it ... -- How it works ... -- Generating interactive displays in the Jupyter Notebook -- Getting ready -- How to do it ... -- How it works ... -- Object-oriented graph creation using Artist objects -- Getting ready -- How to do it ... -- How it works ... -- Creating a map with cartopy -- Getting ready -- How to do it ... -- How it works ... -- Chapter 4: Data Wrangling with pandas -- Creating Series objects -- Getting ready -- How to do it ... -- How it works ... -- Creating DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Inserting and deleting columns to a DataFrame -- Getting ready -- How to do it ... -- How it works ... -- Inserting and deleting rows to a DataFrame -- Getting ready -- How to do it ... -- How it works ... -- Selecting items by row indexes and column labels -- Getting ready -- How to do it ... -- How it works ... -- Selecting items by integer location -- Getting ready -- How to do it ... -- How it works ... -- Selecting items using mixed indexing -- Getting ready -- How to do it ... -- How it works ... -- Accessing, selecting, and modifying data -- Getting ready -- How to do it ... -- How it works ... -- Selecting rows using Boolean selection -- How to do it ... -- Reading and storing data in different formats -- Getting ready -- How to do it ... -- Working with CSV, text/tabular, and format data -- How it works ... -- Reading a CSV file into a DataFrame -- Specifying the index column when reading a CSV file -- Reading and writing data in Excel format -- Reading and writing JSON files -- Reading HTML data from the web -- Accessing CSV data on the web. Reading and writing from/to SQL databases -- Data displays employing different kinds of visual representation -- Getting ready -- How to do it ... -- How it works ... -- How to apply numerical functions and operations to Series and DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Computing statistical functions on Series and DataFrame objects -- Getting ready -- How to do it ... -- Retrieving summary descriptive statistics -- How it works ... -- Calculating the mean -- Calculating variance and standard deviation -- How to sort data in Series and DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Performing merging, joins, concatenation, and grouping -- Getting ready -- How to do it ... -- How it works ... -- Merging data from multiple pandas objects -- Chapter 5: Matrices and Linear Algebra -- Introduction -- Matrix operations and functions on two-dimensional arrays -- How to do it ... -- Solving linear systems using matrices -- How it works ... -- How to do it ... -- Calculating the null space of a matrix -- How to do it ... -- Calculating the LU decompositions of a matrix -- How to do it ... -- Calculating the QR decomposition of a matrix -- How to do it ... -- Calculating the eigenvalue and eigenvector of a matrix -- How to do it ... -- Diagonalizing a matrix -- How to do it ... -- Calculating the Jordan form of a matrix -- How to do it ... -- Calculating the singular value decomposition of a matrix -- How to do it ... -- Creating a sparse matrix -- How to do it ... -- Computations on top of a sparse matrix -- How to do it ... -- Chapter 6: Solving Equations and Optimization -- Introduction -- Non-linear equations and systems -- Getting ready -- How to do it ... -- How it works ... -- System of equations and how to solve it -- Getting ready -- How to do it ... -- How it works ... -- Choosing the solver used to find the solution of equations. Getting ready -- How to do it ... -- How it works ... -- Solving constrained non-linear optimization problems in several variables -- Getting ready -- How to do it ... -- How it works ... -- Solving one-dimensional optimization problems -- Getting ready -- How to do it ... -- How it works ... -- Solving multidimensional non-linear equations using the Newton-Krylov method -- Getting ready -- How to do it ... -- Solving multidimensional non-linear equations using the Anderson method -- Getting ready -- How to do it ... -- How it works ... -- Finding the best linear fit for a set of data -- Getting ready -- How to do it ... -- How it works ... -- Doing non-linear regression for a set of data -- Getting ready -- How to do it ... -- How it works ... -- Regression -- Getting ready -- How to do it ... -- How it works ... -- Chapter 7: Constants and Special Functions -- Introduction -- Physical and mathematical constants available in SciPy -- Getting ready ... -- How to do it ... -- Using constants in the CODATA database -- Getting ready -- How to do it ... -- Bessel functions -- Getting ready ... -- How to do it ... -- Error functions -- Getting ready ... -- How to do it ... -- Orthogonal polynomials functions -- Getting ready ... -- How to do it ... -- Gamma function -- Getting ready ... -- How to do it ... -- How it works ... -- The Riemann zeta function -- Getting ready -- How to do it ... -- How it works ... -- Airy and Bairy functions -- Getting ready ... -- How to do it ... -- The Bessel and Struve functions -- Getting ready ... -- How to do it ... -- How it works ... -- There's more -- Chapter 8: Calculus, Interpolation, and Differential Equations -- Introduction -- Integration -- Getting ready -- How to do it ... -- How it works ... -- Computing integrals using the Newton-Cotes method -- Computing integrals using a Gaussian quadrature -- Getting ready -- How to do it ... |
| ctrlnum | (OCoLC)1018168528 |
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| discipline | Informatik |
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This book will show you how to get the most out of the SciPy Stack to get a better sense of your data. It includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib ...</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Cover -- Title Page -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting to Know the Tools -- Introduction -- Installing Anaconda on Windows -- How to do it ... -- Installing Anaconda on macOS -- How to do it ... -- Installing Anaconda on Linux -- How to do it ... -- Checking the Anaconda installation -- How to do it ... -- Installing SciPy from a binary distribution on Windows -- How to do it ... -- Installing Python -- Installing the SciPy stack -- Installing SciPy from a binary distribution on macOS -- How to do it ... -- Installing the Xcode command-line tools -- Installing Homebrew -- Installing Python 3 -- Installing the SciPy stack -- Installing SciPy from source on Linux -- How to do it ... -- Installing Python 3 -- Installing the SciPy stack -- Installing optional packages with conda -- Getting ready -- How to do it ... -- Installing packages with pip -- How to do it ... -- Setting up a virtual environment with conda -- Getting ready -- How to do it ... -- Creating a virtual environment for development with conda -- Getting ready -- How to do it ... -- Creating a conda environment with a different version of a package -- Getting ready -- How to do it ... -- Using conda environments to run different versions of Python -- Getting ready -- How to do it ... -- Creating virtual environments with venv -- How to do it ... -- Running SciPy in a script -- Getting ready -- How to do it ... -- Running SciPy in Jupyter -- Getting ready -- How to do it ... -- Running SciPy in Spyder -- Getting ready -- How to do it ... -- Running SciPy in PyCharm -- Getting started -- How to do it ... -- Chapter 2: Getting Started with NumPy -- Introduction -- Creating NumPy arrays -- How to do it ... -- Creating an array from a list.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Specifying the data type for elements in an array -- Creating an empty array with a given shape -- Creating arrays of zeros and ones with a single value -- Creating arrays with equally spaced values -- Creating an array by repeating elements -- Creating an array by tiling another array -- Creating an array with the same shape as another array -- Using object arrays to store heterogeneous data -- See also -- Querying and changing the shape of an array -- How to do it ... -- Storing and retrieving NumPy arrays -- How to do it ... -- Storing a NumPy array in text format -- Storing a NumPy array in CSV format -- Loading an array from a text file -- Storing a single array in binary format -- Storing several arrays in binary format -- Loading arrays stored in NPY binary format -- Indexing -- How to do it ... -- Accessing sub arrays using slices -- Selecting subarrays using an index list -- Indexing with Boolean arrays -- Operations on arrays -- How to do it ... -- Computing a function for all elements of an array -- Doing array operations -- Computing matrix products -- Using masked arrays to represent invalid data -- How to do it ... -- Creating a masked array from an explicit mask -- Creating a masked array from a condition -- Using object arrays to store heterogeneous data -- How to do it ... -- Defining, symbolically, a function operating on arrays -- Getting ready -- How to do it ... -- How it works ... -- Chapter 3: Using Matplotlib to Create Graphs -- Introduction -- Creating two-dimensional plots of functions and data -- Getting ready -- How to do it ... -- How it works ... -- Generating multiple plots in a single figure -- Getting ready -- How to do it ... -- How it works ... -- Setting line styles and markers -- Getting ready -- How to do it ... -- How it works ... -- Using different backends to display graphs -- Getting ready -- How to do it ... -- How it works ...</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Saving plots to disk -- Getting ready -- How to do it ... -- How it works ... -- Annotating graphs -- Getting ready -- How to do it ... -- How it works ... -- Generating histograms and box plots -- Getting ready -- How to do it ... -- How it works ... -- Creating three-dimensional plots -- Getting ready -- How to do it ... -- How it works ... -- Generating interactive displays in the Jupyter Notebook -- Getting ready -- How to do it ... -- How it works ... -- Object-oriented graph creation using Artist objects -- Getting ready -- How to do it ... -- How it works ... -- Creating a map with cartopy -- Getting ready -- How to do it ... -- How it works ... -- Chapter 4: Data Wrangling with pandas -- Creating Series objects -- Getting ready -- How to do it ... -- How it works ... -- Creating DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Inserting and deleting columns to a DataFrame -- Getting ready -- How to do it ... -- How it works ... -- Inserting and deleting rows to a DataFrame -- Getting ready -- How to do it ... -- How it works ... -- Selecting items by row indexes and column labels -- Getting ready -- How to do it ... -- How it works ... -- Selecting items by integer location -- Getting ready -- How to do it ... -- How it works ... -- Selecting items using mixed indexing -- Getting ready -- How to do it ... -- How it works ... -- Accessing, selecting, and modifying data -- Getting ready -- How to do it ... -- How it works ... -- Selecting rows using Boolean selection -- How to do it ... -- Reading and storing data in different formats -- Getting ready -- How to do it ... -- Working with CSV, text/tabular, and format data -- How it works ... -- Reading a CSV file into a DataFrame -- Specifying the index column when reading a CSV file -- Reading and writing data in Excel format -- Reading and writing JSON files -- Reading HTML data from the web -- Accessing CSV data on the web.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Reading and writing from/to SQL databases -- Data displays employing different kinds of visual representation -- Getting ready -- How to do it ... -- How it works ... -- How to apply numerical functions and operations to Series and DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Computing statistical functions on Series and DataFrame objects -- Getting ready -- How to do it ... -- Retrieving summary descriptive statistics -- How it works ... -- Calculating the mean -- Calculating variance and standard deviation -- How to sort data in Series and DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Performing merging, joins, concatenation, and grouping -- Getting ready -- How to do it ... -- How it works ... -- Merging data from multiple pandas objects -- Chapter 5: Matrices and Linear Algebra -- Introduction -- Matrix operations and functions on two-dimensional arrays -- How to do it ... -- Solving linear systems using matrices -- How it works ... -- How to do it ... -- Calculating the null space of a matrix -- How to do it ... -- Calculating the LU decompositions of a matrix -- How to do it ... -- Calculating the QR decomposition of a matrix -- How to do it ... -- Calculating the eigenvalue and eigenvector of a matrix -- How to do it ... -- Diagonalizing a matrix -- How to do it ... -- Calculating the Jordan form of a matrix -- How to do it ... -- Calculating the singular value decomposition of a matrix -- How to do it ... -- Creating a sparse matrix -- How to do it ... -- Computations on top of a sparse matrix -- How to do it ... -- Chapter 6: Solving Equations and Optimization -- Introduction -- Non-linear equations and systems -- Getting ready -- How to do it ... -- How it works ... -- System of equations and how to solve it -- Getting ready -- How to do it ... -- How it works ... -- Choosing the solver used to find the solution of equations.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Getting ready -- How to do it ... -- How it works ... -- Solving constrained non-linear optimization problems in several variables -- Getting ready -- How to do it ... -- How it works ... -- Solving one-dimensional optimization problems -- Getting ready -- How to do it ... -- How it works ... -- Solving multidimensional non-linear equations using the Newton-Krylov method -- Getting ready -- How to do it ... -- Solving multidimensional non-linear equations using the Anderson method -- Getting ready -- How to do it ... -- How it works ... -- Finding the best linear fit for a set of data -- Getting ready -- How to do it ... -- How it works ... -- Doing non-linear regression for a set of data -- Getting ready -- How to do it ... -- How it works ... -- Regression -- Getting ready -- How to do it ... -- How it works ... -- Chapter 7: Constants and Special Functions -- Introduction -- Physical and mathematical constants available in SciPy -- Getting ready ... -- How to do it ... -- Using constants in the CODATA database -- Getting ready -- How to do it ... -- Bessel functions -- Getting ready ... -- How to do it ... -- Error functions -- Getting ready ... -- How to do it ... -- Orthogonal polynomials functions -- Getting ready ... -- How to do it ... -- Gamma function -- Getting ready ... -- How to do it ... -- How it works ... -- The Riemann zeta function -- Getting ready -- How to do it ... -- How it works ... -- Airy and Bairy functions -- Getting ready ... -- How to do it ... -- The Bessel and Struve functions -- Getting ready ... -- How to do it ... -- How it works ... -- There's more -- Chapter 8: Calculus, Interpolation, and Differential Equations -- Introduction -- Integration -- Getting ready -- How to do it ... -- How it works ... -- Computing integrals using the Newton-Cotes method -- Computing 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| id | ZDB-4-EBA-on1018168528 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:28:10Z |
| institution | BVB |
| isbn | 9781788295819 1788295811 1788291468 9781788291460 |
| language | English |
| oclc_num | 1018168528 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (381 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Ayyadevara, Kishore. SciPy Recipes : a cookbook with over 110 proven recipes for performing mathematical and scientific computations. Birmingham : Packt Publishing, 2017. 1 online resource (381 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. With the SciPy Stack, you get the power to effectively manipulate and process your data using the popular Python language. This book will show you how to get the most out of the SciPy Stack to get a better sense of your data. It includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib ... Cover -- Title Page -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting to Know the Tools -- Introduction -- Installing Anaconda on Windows -- How to do it ... -- Installing Anaconda on macOS -- How to do it ... -- Installing Anaconda on Linux -- How to do it ... -- Checking the Anaconda installation -- How to do it ... -- Installing SciPy from a binary distribution on Windows -- How to do it ... -- Installing Python -- Installing the SciPy stack -- Installing SciPy from a binary distribution on macOS -- How to do it ... -- Installing the Xcode command-line tools -- Installing Homebrew -- Installing Python 3 -- Installing the SciPy stack -- Installing SciPy from source on Linux -- How to do it ... -- Installing Python 3 -- Installing the SciPy stack -- Installing optional packages with conda -- Getting ready -- How to do it ... -- Installing packages with pip -- How to do it ... -- Setting up a virtual environment with conda -- Getting ready -- How to do it ... -- Creating a virtual environment for development with conda -- Getting ready -- How to do it ... -- Creating a conda environment with a different version of a package -- Getting ready -- How to do it ... -- Using conda environments to run different versions of Python -- Getting ready -- How to do it ... -- Creating virtual environments with venv -- How to do it ... -- Running SciPy in a script -- Getting ready -- How to do it ... -- Running SciPy in Jupyter -- Getting ready -- How to do it ... -- Running SciPy in Spyder -- Getting ready -- How to do it ... -- Running SciPy in PyCharm -- Getting started -- How to do it ... -- Chapter 2: Getting Started with NumPy -- Introduction -- Creating NumPy arrays -- How to do it ... -- Creating an array from a list. Specifying the data type for elements in an array -- Creating an empty array with a given shape -- Creating arrays of zeros and ones with a single value -- Creating arrays with equally spaced values -- Creating an array by repeating elements -- Creating an array by tiling another array -- Creating an array with the same shape as another array -- Using object arrays to store heterogeneous data -- See also -- Querying and changing the shape of an array -- How to do it ... -- Storing and retrieving NumPy arrays -- How to do it ... -- Storing a NumPy array in text format -- Storing a NumPy array in CSV format -- Loading an array from a text file -- Storing a single array in binary format -- Storing several arrays in binary format -- Loading arrays stored in NPY binary format -- Indexing -- How to do it ... -- Accessing sub arrays using slices -- Selecting subarrays using an index list -- Indexing with Boolean arrays -- Operations on arrays -- How to do it ... -- Computing a function for all elements of an array -- Doing array operations -- Computing matrix products -- Using masked arrays to represent invalid data -- How to do it ... -- Creating a masked array from an explicit mask -- Creating a masked array from a condition -- Using object arrays to store heterogeneous data -- How to do it ... -- Defining, symbolically, a function operating on arrays -- Getting ready -- How to do it ... -- How it works ... -- Chapter 3: Using Matplotlib to Create Graphs -- Introduction -- Creating two-dimensional plots of functions and data -- Getting ready -- How to do it ... -- How it works ... -- Generating multiple plots in a single figure -- Getting ready -- How to do it ... -- How it works ... -- Setting line styles and markers -- Getting ready -- How to do it ... -- How it works ... -- Using different backends to display graphs -- Getting ready -- How to do it ... -- How it works ... Saving plots to disk -- Getting ready -- How to do it ... -- How it works ... -- Annotating graphs -- Getting ready -- How to do it ... -- How it works ... -- Generating histograms and box plots -- Getting ready -- How to do it ... -- How it works ... -- Creating three-dimensional plots -- Getting ready -- How to do it ... -- How it works ... -- Generating interactive displays in the Jupyter Notebook -- Getting ready -- How to do it ... -- How it works ... -- Object-oriented graph creation using Artist objects -- Getting ready -- How to do it ... -- How it works ... -- Creating a map with cartopy -- Getting ready -- How to do it ... -- How it works ... -- Chapter 4: Data Wrangling with pandas -- Creating Series objects -- Getting ready -- How to do it ... -- How it works ... -- Creating DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Inserting and deleting columns to a DataFrame -- Getting ready -- How to do it ... -- How it works ... -- Inserting and deleting rows to a DataFrame -- Getting ready -- How to do it ... -- How it works ... -- Selecting items by row indexes and column labels -- Getting ready -- How to do it ... -- How it works ... -- Selecting items by integer location -- Getting ready -- How to do it ... -- How it works ... -- Selecting items using mixed indexing -- Getting ready -- How to do it ... -- How it works ... -- Accessing, selecting, and modifying data -- Getting ready -- How to do it ... -- How it works ... -- Selecting rows using Boolean selection -- How to do it ... -- Reading and storing data in different formats -- Getting ready -- How to do it ... -- Working with CSV, text/tabular, and format data -- How it works ... -- Reading a CSV file into a DataFrame -- Specifying the index column when reading a CSV file -- Reading and writing data in Excel format -- Reading and writing JSON files -- Reading HTML data from the web -- Accessing CSV data on the web. Reading and writing from/to SQL databases -- Data displays employing different kinds of visual representation -- Getting ready -- How to do it ... -- How it works ... -- How to apply numerical functions and operations to Series and DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Computing statistical functions on Series and DataFrame objects -- Getting ready -- How to do it ... -- Retrieving summary descriptive statistics -- How it works ... -- Calculating the mean -- Calculating variance and standard deviation -- How to sort data in Series and DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Performing merging, joins, concatenation, and grouping -- Getting ready -- How to do it ... -- How it works ... -- Merging data from multiple pandas objects -- Chapter 5: Matrices and Linear Algebra -- Introduction -- Matrix operations and functions on two-dimensional arrays -- How to do it ... -- Solving linear systems using matrices -- How it works ... -- How to do it ... -- Calculating the null space of a matrix -- How to do it ... -- Calculating the LU decompositions of a matrix -- How to do it ... -- Calculating the QR decomposition of a matrix -- How to do it ... -- Calculating the eigenvalue and eigenvector of a matrix -- How to do it ... -- Diagonalizing a matrix -- How to do it ... -- Calculating the Jordan form of a matrix -- How to do it ... -- Calculating the singular value decomposition of a matrix -- How to do it ... -- Creating a sparse matrix -- How to do it ... -- Computations on top of a sparse matrix -- How to do it ... -- Chapter 6: Solving Equations and Optimization -- Introduction -- Non-linear equations and systems -- Getting ready -- How to do it ... -- How it works ... -- System of equations and how to solve it -- Getting ready -- How to do it ... -- How it works ... -- Choosing the solver used to find the solution of equations. Getting ready -- How to do it ... -- How it works ... -- Solving constrained non-linear optimization problems in several variables -- Getting ready -- How to do it ... -- How it works ... -- Solving one-dimensional optimization problems -- Getting ready -- How to do it ... -- How it works ... -- Solving multidimensional non-linear equations using the Newton-Krylov method -- Getting ready -- How to do it ... -- Solving multidimensional non-linear equations using the Anderson method -- Getting ready -- How to do it ... -- How it works ... -- Finding the best linear fit for a set of data -- Getting ready -- How to do it ... -- How it works ... -- Doing non-linear regression for a set of data -- Getting ready -- How to do it ... -- How it works ... -- Regression -- Getting ready -- How to do it ... -- How it works ... -- Chapter 7: Constants and Special Functions -- Introduction -- Physical and mathematical constants available in SciPy -- Getting ready ... -- How to do it ... -- Using constants in the CODATA database -- Getting ready -- How to do it ... -- Bessel functions -- Getting ready ... -- How to do it ... -- Error functions -- Getting ready ... -- How to do it ... -- Orthogonal polynomials functions -- Getting ready ... -- How to do it ... -- Gamma function -- Getting ready ... -- How to do it ... -- How it works ... -- The Riemann zeta function -- Getting ready -- How to do it ... -- How it works ... -- Airy and Bairy functions -- Getting ready ... -- How to do it ... -- The Bessel and Struve functions -- Getting ready ... -- How to do it ... -- How it works ... -- There's more -- Chapter 8: Calculus, Interpolation, and Differential Equations -- Introduction -- Integration -- Getting ready -- How to do it ... -- How it works ... -- Computing integrals using the Newton-Cotes method -- Computing integrals using a Gaussian quadrature -- Getting ready -- How to do it ... Python. Numerical analysis. http://id.loc.gov/authorities/subjects/sh85093237 Mathematics Data processing. http://id.loc.gov/authorities/subjects/sh85082146 Analyse numérique. Mathématiques Informatique. Mathematics Data processing fast Numerical analysis fast Martins, Luiz Felipe. Oliva Ramos, Ruben. Oliva, Tomas. Wu, Ke. has work: SciPy recipes (Text) https://id.oclc.org/worldcat/entity/E39PCG6RRDtyKcKqDFbXHXrWH3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Ayyadevara, Kishore. SciPy Recipes : A cookbook with over 110 proven recipes for performing mathematical and scientific computations. Birmingham : Packt Publishing, ©2017 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1672102 Volltext |
| spellingShingle | Ayyadevara, Kishore SciPy Recipes : a cookbook with over 110 proven recipes for performing mathematical and scientific computations. Cover -- Title Page -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting to Know the Tools -- Introduction -- Installing Anaconda on Windows -- How to do it ... -- Installing Anaconda on macOS -- How to do it ... -- Installing Anaconda on Linux -- How to do it ... -- Checking the Anaconda installation -- How to do it ... -- Installing SciPy from a binary distribution on Windows -- How to do it ... -- Installing Python -- Installing the SciPy stack -- Installing SciPy from a binary distribution on macOS -- How to do it ... -- Installing the Xcode command-line tools -- Installing Homebrew -- Installing Python 3 -- Installing the SciPy stack -- Installing SciPy from source on Linux -- How to do it ... -- Installing Python 3 -- Installing the SciPy stack -- Installing optional packages with conda -- Getting ready -- How to do it ... -- Installing packages with pip -- How to do it ... -- Setting up a virtual environment with conda -- Getting ready -- How to do it ... -- Creating a virtual environment for development with conda -- Getting ready -- How to do it ... -- Creating a conda environment with a different version of a package -- Getting ready -- How to do it ... -- Using conda environments to run different versions of Python -- Getting ready -- How to do it ... -- Creating virtual environments with venv -- How to do it ... -- Running SciPy in a script -- Getting ready -- How to do it ... -- Running SciPy in Jupyter -- Getting ready -- How to do it ... -- Running SciPy in Spyder -- Getting ready -- How to do it ... -- Running SciPy in PyCharm -- Getting started -- How to do it ... -- Chapter 2: Getting Started with NumPy -- Introduction -- Creating NumPy arrays -- How to do it ... -- Creating an array from a list. Specifying the data type for elements in an array -- Creating an empty array with a given shape -- Creating arrays of zeros and ones with a single value -- Creating arrays with equally spaced values -- Creating an array by repeating elements -- Creating an array by tiling another array -- Creating an array with the same shape as another array -- Using object arrays to store heterogeneous data -- See also -- Querying and changing the shape of an array -- How to do it ... -- Storing and retrieving NumPy arrays -- How to do it ... -- Storing a NumPy array in text format -- Storing a NumPy array in CSV format -- Loading an array from a text file -- Storing a single array in binary format -- Storing several arrays in binary format -- Loading arrays stored in NPY binary format -- Indexing -- How to do it ... -- Accessing sub arrays using slices -- Selecting subarrays using an index list -- Indexing with Boolean arrays -- Operations on arrays -- How to do it ... -- Computing a function for all elements of an array -- Doing array operations -- Computing matrix products -- Using masked arrays to represent invalid data -- How to do it ... -- Creating a masked array from an explicit mask -- Creating a masked array from a condition -- Using object arrays to store heterogeneous data -- How to do it ... -- Defining, symbolically, a function operating on arrays -- Getting ready -- How to do it ... -- How it works ... -- Chapter 3: Using Matplotlib to Create Graphs -- Introduction -- Creating two-dimensional plots of functions and data -- Getting ready -- How to do it ... -- How it works ... -- Generating multiple plots in a single figure -- Getting ready -- How to do it ... -- How it works ... -- Setting line styles and markers -- Getting ready -- How to do it ... -- How it works ... -- Using different backends to display graphs -- Getting ready -- How to do it ... -- How it works ... Saving plots to disk -- Getting ready -- How to do it ... -- How it works ... -- Annotating graphs -- Getting ready -- How to do it ... -- How it works ... -- Generating histograms and box plots -- Getting ready -- How to do it ... -- How it works ... -- Creating three-dimensional plots -- Getting ready -- How to do it ... -- How it works ... -- Generating interactive displays in the Jupyter Notebook -- Getting ready -- How to do it ... -- How it works ... -- Object-oriented graph creation using Artist objects -- Getting ready -- How to do it ... -- How it works ... -- Creating a map with cartopy -- Getting ready -- How to do it ... -- How it works ... -- Chapter 4: Data Wrangling with pandas -- Creating Series objects -- Getting ready -- How to do it ... -- How it works ... -- Creating DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Inserting and deleting columns to a DataFrame -- Getting ready -- How to do it ... -- How it works ... -- Inserting and deleting rows to a DataFrame -- Getting ready -- How to do it ... -- How it works ... -- Selecting items by row indexes and column labels -- Getting ready -- How to do it ... -- How it works ... -- Selecting items by integer location -- Getting ready -- How to do it ... -- How it works ... -- Selecting items using mixed indexing -- Getting ready -- How to do it ... -- How it works ... -- Accessing, selecting, and modifying data -- Getting ready -- How to do it ... -- How it works ... -- Selecting rows using Boolean selection -- How to do it ... -- Reading and storing data in different formats -- Getting ready -- How to do it ... -- Working with CSV, text/tabular, and format data -- How it works ... -- Reading a CSV file into a DataFrame -- Specifying the index column when reading a CSV file -- Reading and writing data in Excel format -- Reading and writing JSON files -- Reading HTML data from the web -- Accessing CSV data on the web. Reading and writing from/to SQL databases -- Data displays employing different kinds of visual representation -- Getting ready -- How to do it ... -- How it works ... -- How to apply numerical functions and operations to Series and DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Computing statistical functions on Series and DataFrame objects -- Getting ready -- How to do it ... -- Retrieving summary descriptive statistics -- How it works ... -- Calculating the mean -- Calculating variance and standard deviation -- How to sort data in Series and DataFrame objects -- Getting ready -- How to do it ... -- How it works ... -- Performing merging, joins, concatenation, and grouping -- Getting ready -- How to do it ... -- How it works ... -- Merging data from multiple pandas objects -- Chapter 5: Matrices and Linear Algebra -- Introduction -- Matrix operations and functions on two-dimensional arrays -- How to do it ... -- Solving linear systems using matrices -- How it works ... -- How to do it ... -- Calculating the null space of a matrix -- How to do it ... -- Calculating the LU decompositions of a matrix -- How to do it ... -- Calculating the QR decomposition of a matrix -- How to do it ... -- Calculating the eigenvalue and eigenvector of a matrix -- How to do it ... -- Diagonalizing a matrix -- How to do it ... -- Calculating the Jordan form of a matrix -- How to do it ... -- Calculating the singular value decomposition of a matrix -- How to do it ... -- Creating a sparse matrix -- How to do it ... -- Computations on top of a sparse matrix -- How to do it ... -- Chapter 6: Solving Equations and Optimization -- Introduction -- Non-linear equations and systems -- Getting ready -- How to do it ... -- How it works ... -- System of equations and how to solve it -- Getting ready -- How to do it ... -- How it works ... -- Choosing the solver used to find the solution of equations. Getting ready -- How to do it ... -- How it works ... -- Solving constrained non-linear optimization problems in several variables -- Getting ready -- How to do it ... -- How it works ... -- Solving one-dimensional optimization problems -- Getting ready -- How to do it ... -- How it works ... -- Solving multidimensional non-linear equations using the Newton-Krylov method -- Getting ready -- How to do it ... -- Solving multidimensional non-linear equations using the Anderson method -- Getting ready -- How to do it ... -- How it works ... -- Finding the best linear fit for a set of data -- Getting ready -- How to do it ... -- How it works ... -- Doing non-linear regression for a set of data -- Getting ready -- How to do it ... -- How it works ... -- Regression -- Getting ready -- How to do it ... -- How it works ... -- Chapter 7: Constants and Special Functions -- Introduction -- Physical and mathematical constants available in SciPy -- Getting ready ... -- How to do it ... -- Using constants in the CODATA database -- Getting ready -- How to do it ... -- Bessel functions -- Getting ready ... -- How to do it ... -- Error functions -- Getting ready ... -- How to do it ... -- Orthogonal polynomials functions -- Getting ready ... -- How to do it ... -- Gamma function -- Getting ready ... -- How to do it ... -- How it works ... -- The Riemann zeta function -- Getting ready -- How to do it ... -- How it works ... -- Airy and Bairy functions -- Getting ready ... -- How to do it ... -- The Bessel and Struve functions -- Getting ready ... -- How to do it ... -- How it works ... -- There's more -- Chapter 8: Calculus, Interpolation, and Differential Equations -- Introduction -- Integration -- Getting ready -- How to do it ... -- How it works ... -- Computing integrals using the Newton-Cotes method -- Computing integrals using a Gaussian quadrature -- Getting ready -- How to do it ... Python. Numerical analysis. http://id.loc.gov/authorities/subjects/sh85093237 Mathematics Data processing. http://id.loc.gov/authorities/subjects/sh85082146 Analyse numérique. Mathématiques Informatique. Mathematics Data processing fast Numerical analysis fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85093237 http://id.loc.gov/authorities/subjects/sh85082146 |
| title | SciPy Recipes : a cookbook with over 110 proven recipes for performing mathematical and scientific computations. |
| title_auth | SciPy Recipes : a cookbook with over 110 proven recipes for performing mathematical and scientific computations. |
| title_exact_search | SciPy Recipes : a cookbook with over 110 proven recipes for performing mathematical and scientific computations. |
| title_full | SciPy Recipes : a cookbook with over 110 proven recipes for performing mathematical and scientific computations. |
| title_fullStr | SciPy Recipes : a cookbook with over 110 proven recipes for performing mathematical and scientific computations. |
| title_full_unstemmed | SciPy Recipes : a cookbook with over 110 proven recipes for performing mathematical and scientific computations. |
| title_short | SciPy Recipes : |
| title_sort | scipy recipes a cookbook with over 110 proven recipes for performing mathematical and scientific computations |
| title_sub | a cookbook with over 110 proven recipes for performing mathematical and scientific computations. |
| topic | Python. Numerical analysis. http://id.loc.gov/authorities/subjects/sh85093237 Mathematics Data processing. http://id.loc.gov/authorities/subjects/sh85082146 Analyse numérique. Mathématiques Informatique. Mathematics Data processing fast Numerical analysis fast |
| topic_facet | Python. Numerical analysis. Mathematics Data processing. Analyse numérique. Mathématiques Informatique. Mathematics Data processing Numerical analysis |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1672102 |
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